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Record W2795704652 · doi:10.23919/icmu.2017.8330076

A three-dimensional smartphone positioning method using a spinning magnet marker

2017· article· en· W2795704652 on OpenAlexaff
Kosuke Watanabe, Kei Hiroi, T. Kamiyama, Hiroyuki Sano, Masakatsu Tsukamoto, Masaji Katagiri, Daizo Ikeda, Katsuhiko Kaji, Nobuo Kawaguchi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIndoor and Outdoor Localization Technologies
Canadian institutionsGLS Industries (Canada)
FundersJapan Society for the Promotion of Science
KeywordsSpinningAzimuthMagnetPosition (finance)Computer scienceMagnetic fieldGyroscopeTrajectoryElevation (ballistics)AcousticsComputer visionPhysicsOpticsEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

We propose a method of detecting the precise three-dimensional position of a smartphone using a Spinning Magnet Marker (SMM). An SMM is a device that generates a dynamic magnetic field by spinning a strong magnet with a motor. In the proposed method, the magnetic sensor of a smartphone detects the magnetic field generated by an SMM, and the three-imensional position of the smartphone is estimated with an accuracy of better than several tens of centimeters based on the magnetic field and the motor angle of the SMM. It is expected that such precise three-dimensional positioning will enable not only better navigation of users to their destinations but also a better understanding of human behavior. First, we construct theoretical equations relating the magnetic field generated by the SMM to the three-dimensional position of the smartphone in three-dimensional polar coordinates. Second, we evaluate the estimation accuracy of the proposed method with the distance between the SMM and the smartphone fixed at 1.0 m. The azimuth is estimated with a mean error of within 11 degrees, and the elevation is estimated with a mean error of within 10 degrees. The distance is estimated with a mean error of within 19 cm at distances of up to 3.0 m.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.606
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.273
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2017
Admission routes1
Has abstractyes

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